Overview

Dataset statistics

Number of variables16
Number of observations549304
Missing cells116573
Missing cells (%)1.3%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory71.2 MiB
Average record size in memory136.0 B

Variable types

Numeric5
Text8
Categorical3

Alerts

transmission is highly imbalanced (89.1%)Imbalance
interior is highly imbalanced (50.9%)Imbalance
make has 9259 (1.7%) missing valuesMissing
model has 9357 (1.7%) missing valuesMissing
trim has 9561 (1.7%) missing valuesMissing
body has 11767 (2.1%) missing valuesMissing
transmission has 64016 (11.7%) missing valuesMissing
condition has 11001 (2.0%) missing valuesMissing

Reproduction

Analysis started2024-04-01 15:59:08.684941
Analysis finished2024-04-01 15:59:48.915469
Duration40.23 seconds
Software versionydata-profiling vv4.7.0
Download configurationconfig.json

Variables

year
Real number (ℝ)

Distinct16
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2010.2606
Minimum2000
Maximum2015
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2024-04-01T12:59:49.174414image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum2000
5-th percentile2003
Q12008
median2012
Q32013
95-th percentile2014
Maximum2015
Range15
Interquartile range (IQR)5

Descriptive statistics

Standard deviation3.6129215
Coefficient of variation (CV)0.0017972403
Kurtosis-0.10447531
Mean2010.2606
Median Absolute Deviation (MAD)2
Skewness-0.94383354
Sum1.1042442 × 109
Variance13.053202
MonotonicityNot monotonic
2024-04-01T12:59:49.366136image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=16)
ValueCountFrequency (%)
2012 102315
18.6%
2013 98168
17.9%
2014 81070
14.8%
2011 48548
8.8%
2008 31502
 
5.7%
2007 30845
 
5.6%
2006 26913
 
4.9%
2010 26485
 
4.8%
2005 21394
 
3.9%
2009 20594
 
3.7%
Other values (6) 61470
11.2%
ValueCountFrequency (%)
2000 5227
 
1.0%
2001 6468
 
1.2%
2002 9715
 
1.8%
2003 13281
2.4%
2004 17342
3.2%
2005 21394
3.9%
2006 26913
4.9%
2007 30845
5.6%
2008 31502
5.7%
2009 20594
3.7%
ValueCountFrequency (%)
2015 9437
 
1.7%
2014 81070
14.8%
2013 98168
17.9%
2012 102315
18.6%
2011 48548
8.8%
2010 26485
 
4.8%
2009 20594
 
3.7%
2008 31502
 
5.7%
2007 30845
 
5.6%
2006 26913
 
4.9%

make
Text

MISSING 

Distinct92
Distinct (%)< 0.1%
Missing9259
Missing (%)1.7%
Memory size8.4 MiB
2024-04-01T12:59:49.632657image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length13
Median length11
Mean length5.9939524
Min length2

Characters and Unicode

Total characters3237004
Distinct characters49
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique6 ?
Unique (%)< 0.1%

Sample

1st rowKia
2nd rowKia
3rd rowBMW
4th rowVolvo
5th rowBMW
ValueCountFrequency (%)
ford 92853
17.1%
chevrolet 59908
 
11.1%
nissan 53525
 
9.9%
toyota 38741
 
7.1%
dodge 30770
 
5.7%
honda 26255
 
4.8%
hyundai 21836
 
4.0%
bmw 20506
 
3.8%
kia 18078
 
3.3%
chrysler 17379
 
3.2%
Other values (52) 162077
29.9%
2024-04-01T12:59:50.146191image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
o 322325
 
10.0%
e 295829
 
9.1%
a 231247
 
7.1%
r 226956
 
7.0%
d 212574
 
6.6%
n 183457
 
5.7%
i 183078
 
5.7%
s 176214
 
5.4%
t 125908
 
3.9%
l 114889
 
3.5%
Other values (39) 1164527
36.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3237004
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
o 322325
 
10.0%
e 295829
 
9.1%
a 231247
 
7.1%
r 226956
 
7.0%
d 212574
 
6.6%
n 183457
 
5.7%
i 183078
 
5.7%
s 176214
 
5.4%
t 125908
 
3.9%
l 114889
 
3.5%
Other values (39) 1164527
36.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3237004
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
o 322325
 
10.0%
e 295829
 
9.1%
a 231247
 
7.1%
r 226956
 
7.0%
d 212574
 
6.6%
n 183457
 
5.7%
i 183078
 
5.7%
s 176214
 
5.4%
t 125908
 
3.9%
l 114889
 
3.5%
Other values (39) 1164527
36.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3237004
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
o 322325
 
10.0%
e 295829
 
9.1%
a 231247
 
7.1%
r 226956
 
7.0%
d 212574
 
6.6%
n 183457
 
5.7%
i 183078
 
5.7%
s 176214
 
5.4%
t 125908
 
3.9%
l 114889
 
3.5%
Other values (39) 1164527
36.0%

model
Text

MISSING 

Distinct894
Distinct (%)0.2%
Missing9357
Missing (%)1.7%
Memory size8.4 MiB
2024-04-01T12:59:50.578304image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length29
Median length23
Mean length6.7663215
Min length1

Characters and Unicode

Total characters3653455
Distinct characters66
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique56 ?
Unique (%)< 0.1%

Sample

1st rowSorento
2nd rowSorento
3rd row3 Series
4th rowS60
5th row6 Series Gran Coupe
ValueCountFrequency (%)
altima 19279
 
2.9%
series 15144
 
2.3%
grand 14584
 
2.2%
f-150 14340
 
2.2%
1500 14311
 
2.2%
fusion 13639
 
2.1%
camry 12933
 
2.0%
escape 12027
 
1.8%
focus 10463
 
1.6%
g 9333
 
1.4%
Other values (682) 522939
79.4%
2024-04-01T12:59:51.347001image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 371398
 
10.2%
r 271372
 
7.4%
e 265203
 
7.3%
o 192334
 
5.3%
n 182740
 
5.0%
i 167332
 
4.6%
s 147938
 
4.0%
t 134679
 
3.7%
l 130333
 
3.6%
C 120882
 
3.3%
Other values (56) 1669244
45.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 3653455
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 371398
 
10.2%
r 271372
 
7.4%
e 265203
 
7.3%
o 192334
 
5.3%
n 182740
 
5.0%
i 167332
 
4.6%
s 147938
 
4.0%
t 134679
 
3.7%
l 130333
 
3.6%
C 120882
 
3.3%
Other values (56) 1669244
45.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 3653455
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 371398
 
10.2%
r 271372
 
7.4%
e 265203
 
7.3%
o 192334
 
5.3%
n 182740
 
5.0%
i 167332
 
4.6%
s 147938
 
4.0%
t 134679
 
3.7%
l 130333
 
3.6%
C 120882
 
3.3%
Other values (56) 1669244
45.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 3653455
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 371398
 
10.2%
r 271372
 
7.4%
e 265203
 
7.3%
o 192334
 
5.3%
n 182740
 
5.0%
i 167332
 
4.6%
s 147938
 
4.0%
t 134679
 
3.7%
l 130333
 
3.6%
C 120882
 
3.3%
Other values (56) 1669244
45.7%

trim
Text

MISSING 

Distinct1803
Distinct (%)0.3%
Missing9561
Missing (%)1.7%
Memory size8.4 MiB
2024-04-01T12:59:51.892974image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length46
Median length37
Mean length4.7489898
Min length1

Characters and Unicode

Total characters2563234
Distinct characters71
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique207 ?
Unique (%)< 0.1%

Sample

1st rowLX
2nd rowLX
3rd row328i SULEV
4th rowT5
5th row650i
ValueCountFrequency (%)
base 54472
 
8.2%
se 48024
 
7.2%
s 30261
 
4.6%
lx 20748
 
3.1%
limited 20404
 
3.1%
lt 20112
 
3.0%
2.5 18853
 
2.8%
xlt 18441
 
2.8%
ls 17551
 
2.6%
sport 17493
 
2.6%
Other values (892) 396465
59.8%
2024-04-01T12:59:52.626736image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
L 211724
 
8.3%
S 204581
 
8.0%
e 152564
 
6.0%
i 134145
 
5.2%
E 124980
 
4.9%
123081
 
4.8%
T 120090
 
4.7%
a 106540
 
4.2%
r 97083
 
3.8%
X 89373
 
3.5%
Other values (61) 1199073
46.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2563234
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
L 211724
 
8.3%
S 204581
 
8.0%
e 152564
 
6.0%
i 134145
 
5.2%
E 124980
 
4.9%
123081
 
4.8%
T 120090
 
4.7%
a 106540
 
4.2%
r 97083
 
3.8%
X 89373
 
3.5%
Other values (61) 1199073
46.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2563234
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
L 211724
 
8.3%
S 204581
 
8.0%
e 152564
 
6.0%
i 134145
 
5.2%
E 124980
 
4.9%
123081
 
4.8%
T 120090
 
4.7%
a 106540
 
4.2%
r 97083
 
3.8%
X 89373
 
3.5%
Other values (61) 1199073
46.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2563234
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
L 211724
 
8.3%
S 204581
 
8.0%
e 152564
 
6.0%
i 134145
 
5.2%
E 124980
 
4.9%
123081
 
4.8%
T 120090
 
4.7%
a 106540
 
4.2%
r 97083
 
3.8%
X 89373
 
3.5%
Other values (61) 1199073
46.8%

body
Text

MISSING 

Distinct87
Distinct (%)< 0.1%
Missing11767
Missing (%)2.1%
Memory size8.4 MiB
2024-04-01T12:59:52.888540image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length23
Median length5
Mean length5.2779306
Min length3

Characters and Unicode

Total characters2837083
Distinct characters48
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique5 ?
Unique (%)< 0.1%

Sample

1st rowSUV
2nd rowSUV
3rd rowSedan
4th rowSedan
5th rowSedan
ValueCountFrequency (%)
sedan 244069
41.9%
suv 142309
24.4%
cab 32622
 
5.6%
hatchback 26065
 
4.5%
minivan 25529
 
4.4%
coupe 19337
 
3.3%
crew 16393
 
2.8%
wagon 15991
 
2.7%
convertible 10599
 
1.8%
g 9333
 
1.6%
Other values (33) 40071
 
6.9%
2024-04-01T12:59:53.457639image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 392077
13.8%
e 345501
12.2%
n 333296
11.7%
S 332816
11.7%
d 256884
 
9.1%
V 123431
 
4.4%
U 117947
 
4.2%
C 77228
 
2.7%
b 76442
 
2.7%
s 73109
 
2.6%
Other values (38) 708352
25.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2837083
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 392077
13.8%
e 345501
12.2%
n 333296
11.7%
S 332816
11.7%
d 256884
 
9.1%
V 123431
 
4.4%
U 117947
 
4.2%
C 77228
 
2.7%
b 76442
 
2.7%
s 73109
 
2.6%
Other values (38) 708352
25.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2837083
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 392077
13.8%
e 345501
12.2%
n 333296
11.7%
S 332816
11.7%
d 256884
 
9.1%
V 123431
 
4.4%
U 117947
 
4.2%
C 77228
 
2.7%
b 76442
 
2.7%
s 73109
 
2.6%
Other values (38) 708352
25.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2837083
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 392077
13.8%
e 345501
12.2%
n 333296
11.7%
S 332816
11.7%
d 256884
 
9.1%
V 123431
 
4.4%
U 117947
 
4.2%
C 77228
 
2.7%
b 76442
 
2.7%
s 73109
 
2.6%
Other values (38) 708352
25.0%

transmission
Categorical

IMBALANCE  MISSING 

Distinct4
Distinct (%)< 0.1%
Missing64016
Missing (%)11.7%
Memory size8.4 MiB
automatic
468495 
manual
 
16767
sedan
 
15
Sedan
 
11

Length

Max length9
Median length9
Mean length8.8961338
Min length5

Characters and Unicode

Total characters4317187
Distinct characters13
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowautomatic
2nd rowautomatic
3rd rowautomatic
4th rowautomatic
5th rowautomatic

Common Values

ValueCountFrequency (%)
automatic 468495
85.3%
manual 16767
 
3.1%
sedan 15
 
< 0.1%
Sedan 11
 
< 0.1%
(Missing) 64016
 
11.7%

Length

2024-04-01T12:59:53.711837image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2024-04-01T12:59:53.908517image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
ValueCountFrequency (%)
automatic 468495
96.5%
manual 16767
 
3.5%
sedan 26
 
< 0.1%

Most occurring characters

ValueCountFrequency (%)
a 970550
22.5%
t 936990
21.7%
u 485262
11.2%
m 485262
11.2%
o 468495
10.9%
i 468495
10.9%
c 468495
10.9%
n 16793
 
0.4%
l 16767
 
0.4%
e 26
 
< 0.1%
Other values (3) 52
 
< 0.1%

Most occurring categories

ValueCountFrequency (%)
(unknown) 4317187
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 970550
22.5%
t 936990
21.7%
u 485262
11.2%
m 485262
11.2%
o 468495
10.9%
i 468495
10.9%
c 468495
10.9%
n 16793
 
0.4%
l 16767
 
0.4%
e 26
 
< 0.1%
Other values (3) 52
 
< 0.1%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 4317187
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 970550
22.5%
t 936990
21.7%
u 485262
11.2%
m 485262
11.2%
o 468495
10.9%
i 468495
10.9%
c 468495
10.9%
n 16793
 
0.4%
l 16767
 
0.4%
e 26
 
< 0.1%
Other values (3) 52
 
< 0.1%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 4317187
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 970550
22.5%
t 936990
21.7%
u 485262
11.2%
m 485262
11.2%
o 468495
10.9%
i 468495
10.9%
c 468495
10.9%
n 16793
 
0.4%
l 16767
 
0.4%
e 26
 
< 0.1%
Other values (3) 52
 
< 0.1%

vin
Text

Distinct540830
Distinct (%)98.5%
Missing4
Missing (%)< 0.1%
Memory size8.4 MiB
2024-04-01T12:59:54.641612image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length17
Median length17
Mean length16.99968
Min length9

Characters and Unicode

Total characters9337924
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique532567 ?
Unique (%)97.0%

Sample

1st row5xyktca69fg566472
2nd row5xyktca69fg561319
3rd rowwba3c1c51ek116351
4th rowyv1612tb4f1310987
5th rowwba6b2c57ed129731
ValueCountFrequency (%)
automatic 22
 
< 0.1%
wbanv13588cz57827 5
 
< 0.1%
trusc28n241022003 4
 
< 0.1%
5n1ar1nn2bc632869 4
 
< 0.1%
wddgf56x78f009940 4
 
< 0.1%
1ftfw1cv5afb30053 4
 
< 0.1%
5uxfe43579l274932 4
 
< 0.1%
4t1bk1eb4du026484 3
 
< 0.1%
1g2mb33b56y108249 3
 
< 0.1%
1g1pa5sh7d7298502 3
 
< 0.1%
Other values (540820) 549244
> 99.9%
2024-04-01T12:59:55.853876image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1 901723
 
9.7%
2 623541
 
6.7%
3 603375
 
6.5%
5 585726
 
6.3%
4 564627
 
6.0%
0 489167
 
5.2%
6 478850
 
5.1%
7 451632
 
4.8%
8 447238
 
4.8%
c 378243
 
4.1%
Other values (25) 3813802
40.8%

Most occurring categories

ValueCountFrequency (%)
(unknown) 9337924
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1 901723
 
9.7%
2 623541
 
6.7%
3 603375
 
6.5%
5 585726
 
6.3%
4 564627
 
6.0%
0 489167
 
5.2%
6 478850
 
5.1%
7 451632
 
4.8%
8 447238
 
4.8%
c 378243
 
4.1%
Other values (25) 3813802
40.8%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 9337924
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1 901723
 
9.7%
2 623541
 
6.7%
3 603375
 
6.5%
5 585726
 
6.3%
4 564627
 
6.0%
0 489167
 
5.2%
6 478850
 
5.1%
7 451632
 
4.8%
8 447238
 
4.8%
c 378243
 
4.1%
Other values (25) 3813802
40.8%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 9337924
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1 901723
 
9.7%
2 623541
 
6.7%
3 603375
 
6.5%
5 585726
 
6.3%
4 564627
 
6.0%
0 489167
 
5.2%
6 478850
 
5.1%
7 451632
 
4.8%
8 447238
 
4.8%
c 378243
 
4.1%
Other values (25) 3813802
40.8%

state
Text

Distinct64
Distinct (%)< 0.1%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2024-04-01T12:59:56.130855image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length17
Median length2
Mean length2.00071
Min length2

Characters and Unicode

Total characters1098998
Distinct characters36
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowca
2nd rowca
3rd rowca
4th rowca
5th rowca
ValueCountFrequency (%)
fl 81399
14.8%
ca 71678
13.0%
pa 53484
 
9.7%
tx 45314
 
8.2%
ga 34106
 
6.2%
nj 27506
 
5.0%
il 23323
 
4.2%
oh 21278
 
3.9%
nc 21120
 
3.8%
tn 20740
 
3.8%
Other values (54) 149356
27.2%
2024-04-01T12:59:56.795047image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
a 196030
17.8%
n 108557
9.9%
l 106891
9.7%
c 105938
9.6%
f 81425
 
7.4%
t 67832
 
6.2%
m 59718
 
5.4%
p 56124
 
5.1%
i 54005
 
4.9%
o 49538
 
4.5%
Other values (26) 212940
19.4%

Most occurring categories

ValueCountFrequency (%)
(unknown) 1098998
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 196030
17.8%
n 108557
9.9%
l 106891
9.7%
c 105938
9.6%
f 81425
 
7.4%
t 67832
 
6.2%
m 59718
 
5.4%
p 56124
 
5.1%
i 54005
 
4.9%
o 49538
 
4.5%
Other values (26) 212940
19.4%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 1098998
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 196030
17.8%
n 108557
9.9%
l 106891
9.7%
c 105938
9.6%
f 81425
 
7.4%
t 67832
 
6.2%
m 59718
 
5.4%
p 56124
 
5.1%
i 54005
 
4.9%
o 49538
 
4.5%
Other values (26) 212940
19.4%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 1098998
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 196030
17.8%
n 108557
9.9%
l 106891
9.7%
c 105938
9.6%
f 81425
 
7.4%
t 67832
 
6.2%
m 59718
 
5.4%
p 56124
 
5.1%
i 54005
 
4.9%
o 49538
 
4.5%
Other values (26) 212940
19.4%

condition
Real number (ℝ)

MISSING 

Distinct41
Distinct (%)< 0.1%
Missing11001
Missing (%)2.0%
Infinite0
Infinite (%)0.0%
Mean30.962993
Minimum1
Maximum49
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2024-04-01T12:59:57.067012image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q124
median35
Q342
95-th percentile47
Maximum49
Range48
Interquartile range (IQR)18

Descriptive statistics

Standard deviation13.22967
Coefficient of variation (CV)0.4272736
Kurtosis-0.1411354
Mean30.962993
Median Absolute Deviation (MAD)8
Skewness-0.85929991
Sum16667472
Variance175.02416
MonotonicityNot monotonic
2024-04-01T12:59:57.304946image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=41)
ValueCountFrequency (%)
19 40154
 
7.3%
35 26637
 
4.8%
37 25886
 
4.7%
44 25495
 
4.6%
43 24919
 
4.5%
42 24308
 
4.4%
36 23084
 
4.2%
41 23057
 
4.2%
39 19892
 
3.6%
4 19881
 
3.6%
Other values (31) 284990
51.9%
ValueCountFrequency (%)
1 6008
 
1.1%
2 18296
3.3%
3 10369
1.9%
4 19881
3.6%
5 11212
2.0%
11 82
 
< 0.1%
12 94
 
< 0.1%
13 81
 
< 0.1%
14 126
 
< 0.1%
15 141
 
< 0.1%
ValueCountFrequency (%)
49 13096
2.4%
48 12707
2.3%
47 11361
2.1%
46 12628
2.3%
45 12306
2.2%
44 25495
4.6%
43 24919
4.5%
42 24308
4.4%
41 23057
4.2%
39 19892
3.6%

odometer
Real number (ℝ)

Distinct167981
Distinct (%)30.6%
Missing84
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean66670.544
Minimum1
Maximum999999
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2024-04-01T12:59:57.562885image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile10457
Q128053
median51190
Q396929
95-th percentile164766
Maximum999999
Range999998
Interquartile range (IQR)68876

Descriptive statistics

Standard deviation51626.514
Coefficient of variation (CV)0.77435267
Kurtosis14.181866
Mean66670.544
Median Absolute Deviation (MAD)29545
Skewness1.8452578
Sum3.6616796 × 1010
Variance2.6652969 × 109
MonotonicityNot monotonic
2024-04-01T12:59:58.047228image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
1 1117
 
0.2%
999999 66
 
< 0.1%
10 28
 
< 0.1%
21587 21
 
< 0.1%
8 18
 
< 0.1%
29137 18
 
< 0.1%
21310 18
 
< 0.1%
19926 17
 
< 0.1%
35314 17
 
< 0.1%
36007 17
 
< 0.1%
Other values (167971) 547883
99.7%
(Missing) 84
 
< 0.1%
ValueCountFrequency (%)
1 1117
0.2%
2 17
 
< 0.1%
3 9
 
< 0.1%
4 9
 
< 0.1%
5 17
 
< 0.1%
6 13
 
< 0.1%
7 13
 
< 0.1%
8 18
 
< 0.1%
9 11
 
< 0.1%
10 28
 
< 0.1%
ValueCountFrequency (%)
999999 66
< 0.1%
694978 2
 
< 0.1%
621388 1
 
< 0.1%
580956 1
 
< 0.1%
537334 1
 
< 0.1%
522212 1
 
< 0.1%
500227 1
 
< 0.1%
495757 1
 
< 0.1%
480747 1
 
< 0.1%
471114 1
 
< 0.1%

color
Categorical

Distinct46
Distinct (%)< 0.1%
Missing731
Missing (%)0.1%
Memory size8.4 MiB
black
109658 
white
104934 
silver
82406 
gray
82270 
blue
50254 
Other values (41)
119051 

Length

Max length9
Median length8
Mean length4.6253024
Min length1

Characters and Unicode

Total characters2537316
Distinct characters35
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique26 ?
Unique (%)< 0.1%

Sample

1st rowwhite
2nd rowwhite
3rd rowgray
4th rowwhite
5th rowgray

Common Values

ValueCountFrequency (%)
black 109658
20.0%
white 104934
19.1%
silver 82406
15.0%
gray 82270
15.0%
blue 50254
9.1%
red 42579
 
7.8%
24606
 
4.5%
gold 10698
 
1.9%
green 10051
 
1.8%
beige 8791
 
1.6%
Other values (36) 22326
 
4.1%

Length

2024-04-01T12:59:58.339847image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 109658
20.0%
white 104934
19.1%
silver 82406
15.0%
gray 82270
15.0%
blue 50254
9.2%
red 42579
 
7.8%
24606
 
4.5%
gold 10698
 
2.0%
green 10051
 
1.8%
beige 8791
 
1.6%
Other values (36) 22326
 
4.1%

Most occurring characters

ValueCountFrequency (%)
e 324327
12.8%
l 257510
 
10.1%
r 236856
 
9.3%
i 197834
 
7.8%
a 194948
 
7.7%
b 184016
 
7.3%
g 122578
 
4.8%
w 114241
 
4.5%
c 110610
 
4.4%
k 109699
 
4.3%
Other values (25) 684697
27.0%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2537316
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
e 324327
12.8%
l 257510
 
10.1%
r 236856
 
9.3%
i 197834
 
7.8%
a 194948
 
7.7%
b 184016
 
7.3%
g 122578
 
4.8%
w 114241
 
4.5%
c 110610
 
4.4%
k 109699
 
4.3%
Other values (25) 684697
27.0%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2537316
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
e 324327
12.8%
l 257510
 
10.1%
r 236856
 
9.3%
i 197834
 
7.8%
a 194948
 
7.7%
b 184016
 
7.3%
g 122578
 
4.8%
w 114241
 
4.5%
c 110610
 
4.4%
k 109699
 
4.3%
Other values (25) 684697
27.0%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2537316
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
e 324327
12.8%
l 257510
 
10.1%
r 236856
 
9.3%
i 197834
 
7.8%
a 194948
 
7.7%
b 184016
 
7.3%
g 122578
 
4.8%
w 114241
 
4.5%
c 110610
 
4.4%
k 109699
 
4.3%
Other values (25) 684697
27.0%

interior
Categorical

IMBALANCE 

Distinct17
Distinct (%)< 0.1%
Missing731
Missing (%)0.1%
Memory size8.4 MiB
black
243541 
gray
174572 
beige
58239 
tan
42506 
 
16117
Other values (12)
 
13598

Length

Max length9
Median length5
Mean length4.4096829
Min length1

Characters and Unicode

Total characters2419033
Distinct characters23
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowblack
2nd rowbeige
3rd rowblack
4th rowblack
5th rowblack

Common Values

ValueCountFrequency (%)
black 243541
44.3%
gray 174572
31.8%
beige 58239
 
10.6%
tan 42506
 
7.7%
16117
 
2.9%
brown 8445
 
1.5%
red 1302
 
0.2%
silver 1081
 
0.2%
blue 860
 
0.2%
off-white 480
 
0.1%
Other values (7) 1430
 
0.3%
(Missing) 731
 
0.1%

Length

2024-04-01T12:59:58.612345image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
black 243541
44.4%
gray 174572
31.8%
beige 58239
 
10.6%
tan 42506
 
7.7%
16117
 
2.9%
brown 8445
 
1.5%
red 1302
 
0.2%
silver 1081
 
0.2%
blue 860
 
0.2%
off-white 480
 
0.1%
Other values (7) 1430
 
0.3%

Most occurring characters

ValueCountFrequency (%)
a 460764
19.0%
b 311257
12.9%
l 246141
10.2%
c 243541
10.1%
k 243541
10.1%
g 233659
9.7%
r 186251
7.7%
y 174763
 
7.2%
e 121372
 
5.0%
i 60051
 
2.5%
Other values (13) 137693
 
5.7%

Most occurring categories

ValueCountFrequency (%)
(unknown) 2419033
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
a 460764
19.0%
b 311257
12.9%
l 246141
10.2%
c 243541
10.1%
k 243541
10.1%
g 233659
9.7%
r 186251
7.7%
y 174763
 
7.2%
e 121372
 
5.0%
i 60051
 
2.5%
Other values (13) 137693
 
5.7%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 2419033
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
a 460764
19.0%
b 311257
12.9%
l 246141
10.2%
c 243541
10.1%
k 243541
10.1%
g 233659
9.7%
r 186251
7.7%
y 174763
 
7.2%
e 121372
 
5.0%
i 60051
 
2.5%
Other values (13) 137693
 
5.7%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 2419033
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
a 460764
19.0%
b 311257
12.9%
l 246141
10.2%
c 243541
10.1%
k 243541
10.1%
g 233659
9.7%
r 186251
7.7%
y 174763
 
7.2%
e 121372
 
5.0%
i 60051
 
2.5%
Other values (13) 137693
 
5.7%

seller
Text

Distinct14046
Distinct (%)2.6%
Missing0
Missing (%)0.0%
Memory size8.4 MiB
2024-04-01T12:59:59.208761image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length50
Median length42
Mean length22.992733
Min length3

Characters and Unicode

Total characters12630000
Distinct characters47
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique4878 ?
Unique (%)0.9%

Sample

1st rowkia motors america inc
2nd rowkia motors america inc
3rd rowfinancial services remarketing (lease)
4th rowvolvo na rep/world omni
5th rowfinancial services remarketing (lease)
ValueCountFrequency (%)
inc 85250
 
4.6%
corporation 47760
 
2.6%
services 47721
 
2.6%
credit 46782
 
2.5%
auto 46580
 
2.5%
motor 45706
 
2.5%
llc 45233
 
2.4%
financial 43844
 
2.4%
ford 35704
 
1.9%
remarketing 34655
 
1.9%
Other values (8464) 1368236
74.1%
2024-04-01T13:00:00.322648image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
1316832
 
10.4%
e 1126416
 
8.9%
a 1034095
 
8.2%
r 946432
 
7.5%
n 938616
 
7.4%
i 904340
 
7.2%
o 847048
 
6.7%
t 781370
 
6.2%
c 725514
 
5.7%
s 660727
 
5.2%
Other values (37) 3348610
26.5%

Most occurring categories

ValueCountFrequency (%)
(unknown) 12630000
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
1316832
 
10.4%
e 1126416
 
8.9%
a 1034095
 
8.2%
r 946432
 
7.5%
n 938616
 
7.4%
i 904340
 
7.2%
o 847048
 
6.7%
t 781370
 
6.2%
c 725514
 
5.7%
s 660727
 
5.2%
Other values (37) 3348610
26.5%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 12630000
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
1316832
 
10.4%
e 1126416
 
8.9%
a 1034095
 
8.2%
r 946432
 
7.5%
n 938616
 
7.4%
i 904340
 
7.2%
o 847048
 
6.7%
t 781370
 
6.2%
c 725514
 
5.7%
s 660727
 
5.2%
Other values (37) 3348610
26.5%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 12630000
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
1316832
 
10.4%
e 1126416
 
8.9%
a 1034095
 
8.2%
r 946432
 
7.5%
n 938616
 
7.4%
i 904340
 
7.2%
o 847048
 
6.7%
t 781370
 
6.2%
c 725514
 
5.7%
s 660727
 
5.2%
Other values (37) 3348610
26.5%

mmr
Real number (ℝ)

Distinct1101
Distinct (%)0.2%
Missing38
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13985.554
Minimum25
Maximum182000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2024-04-01T13:00:00.624158image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum25
5-th percentile2150
Q17475
median12400
Q318450
95-th percentile30800
Maximum182000
Range181975
Interquartile range (IQR)10975

Descriptive statistics

Standard deviation9620.9282
Coefficient of variation (CV)0.68791897
Kurtosis11.820587
Mean13985.554
Median Absolute Deviation (MAD)5450
Skewness2.0411483
Sum7.6817895 × 109
Variance92562259
MonotonicityNot monotonic
2024-04-01T13:00:00.882804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12500 1761
 
0.3%
11600 1751
 
0.3%
11650 1746
 
0.3%
12150 1722
 
0.3%
11300 1716
 
0.3%
11850 1716
 
0.3%
11750 1708
 
0.3%
12350 1701
 
0.3%
12700 1701
 
0.3%
11950 1694
 
0.3%
Other values (1091) 532050
96.9%
ValueCountFrequency (%)
25 21
 
< 0.1%
50 34
< 0.1%
75 15
 
< 0.1%
100 22
 
< 0.1%
125 32
< 0.1%
150 28
< 0.1%
175 44
< 0.1%
200 34
< 0.1%
225 36
< 0.1%
250 57
< 0.1%
ValueCountFrequency (%)
182000 1
 
< 0.1%
178000 1
 
< 0.1%
176000 1
 
< 0.1%
172000 1
 
< 0.1%
170000 3
< 0.1%
166000 3
< 0.1%
164000 1
 
< 0.1%
163000 1
 
< 0.1%
162000 1
 
< 0.1%
161000 1
 
< 0.1%

sellingprice
Real number (ℝ)

Distinct1885
Distinct (%)0.3%
Missing12
Missing (%)< 0.1%
Infinite0
Infinite (%)0.0%
Mean13825.543
Minimum1
Maximum230000
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size8.4 MiB
2024-04-01T13:00:01.202227image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile1800
Q17200
median12300
Q318400
95-th percentile30800
Maximum230000
Range229999
Interquartile range (IQR)11200

Descriptive statistics

Standard deviation9694.1377
Coefficient of variation (CV)0.70117593
Kurtosis11.463063
Mean13825.543
Median Absolute Deviation (MAD)5500
Skewness1.9923523
Sum7.5942599 × 109
Variance93976306
MonotonicityNot monotonic
2024-04-01T13:00:01.472111image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
12000 4450
 
0.8%
11000 4450
 
0.8%
13000 4334
 
0.8%
10000 4028
 
0.7%
14000 3898
 
0.7%
11500 3875
 
0.7%
12500 3713
 
0.7%
9000 3685
 
0.7%
10500 3539
 
0.6%
15000 3385
 
0.6%
Other values (1875) 509935
92.8%
ValueCountFrequency (%)
1 3
 
< 0.1%
100 14
 
< 0.1%
150 5
 
< 0.1%
175 4
 
< 0.1%
200 80
 
< 0.1%
225 55
 
< 0.1%
250 146
 
< 0.1%
275 64
 
< 0.1%
300 673
0.1%
325 110
 
< 0.1%
ValueCountFrequency (%)
230000 1
< 0.1%
183000 1
< 0.1%
173000 1
< 0.1%
171500 1
< 0.1%
169500 1
< 0.1%
169000 1
< 0.1%
167000 1
< 0.1%
165000 2
< 0.1%
163000 2
< 0.1%
161000 1
< 0.1%
Distinct3732
Distinct (%)0.7%
Missing12
Missing (%)< 0.1%
Memory size8.4 MiB
2024-04-01T13:00:01.986987image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Length

Max length39
Median length39
Mean length38.998382
Min length4

Characters and Unicode

Total characters21421499
Distinct characters40
Distinct categories1 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique592 ?
Unique (%)0.1%

Sample

1st rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
2nd rowTue Dec 16 2014 12:30:00 GMT-0800 (PST)
3rd rowThu Jan 15 2015 04:30:00 GMT-0800 (PST)
4th rowThu Jan 29 2015 04:30:00 GMT-0800 (PST)
5th rowThu Dec 18 2014 12:30:00 GMT-0800 (PST)
ValueCountFrequency (%)
2015 497293
 
12.9%
gmt-0800 387885
 
10.1%
pst 387885
 
10.1%
wed 163676
 
4.3%
pdt 161381
 
4.2%
gmt-0700 161381
 
4.2%
feb 160453
 
4.2%
tue 160372
 
4.2%
thu 151048
 
3.9%
jan 138223
 
3.6%
Other values (332) 1475291
38.4%
2024-04-01T13:00:02.721101image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Most occurring characters

ValueCountFrequency (%)
0 4738192
22.1%
3295596
15.4%
T 1409952
 
6.6%
: 1098532
 
5.1%
1 1042238
 
4.9%
2 947100
 
4.4%
M 661852
 
3.1%
5 649562
 
3.0%
G 549266
 
2.6%
) 549266
 
2.6%
Other values (30) 6479943
30.2%

Most occurring categories

ValueCountFrequency (%)
(unknown) 21421499
100.0%

Most frequent character per category

(unknown)
ValueCountFrequency (%)
0 4738192
22.1%
3295596
15.4%
T 1409952
 
6.6%
: 1098532
 
5.1%
1 1042238
 
4.9%
2 947100
 
4.4%
M 661852
 
3.1%
5 649562
 
3.0%
G 549266
 
2.6%
) 549266
 
2.6%
Other values (30) 6479943
30.2%

Most occurring scripts

ValueCountFrequency (%)
(unknown) 21421499
100.0%

Most frequent character per script

(unknown)
ValueCountFrequency (%)
0 4738192
22.1%
3295596
15.4%
T 1409952
 
6.6%
: 1098532
 
5.1%
1 1042238
 
4.9%
2 947100
 
4.4%
M 661852
 
3.1%
5 649562
 
3.0%
G 549266
 
2.6%
) 549266
 
2.6%
Other values (30) 6479943
30.2%

Most occurring blocks

ValueCountFrequency (%)
(unknown) 21421499
100.0%

Most frequent character per block

(unknown)
ValueCountFrequency (%)
0 4738192
22.1%
3295596
15.4%
T 1409952
 
6.6%
: 1098532
 
5.1%
1 1042238
 
4.9%
2 947100
 
4.4%
M 661852
 
3.1%
5 649562
 
3.0%
G 549266
 
2.6%
) 549266
 
2.6%
Other values (30) 6479943
30.2%

Interactions

2024-04-01T12:59:40.001381image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:33.319511image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:34.735287image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:36.375311image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:38.346354image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:40.280983image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:33.598635image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:35.206804image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:36.695905image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:38.755575image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:40.575899image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:33.821121image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:35.494544image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:37.050208image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:39.056512image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:40.906309image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:34.044704image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:35.812541image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:37.643458image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:39.403807image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:41.192207image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:34.411361image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:36.109410image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:38.047315image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
2024-04-01T12:59:39.732042image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/

Missing values

2024-04-01T12:59:41.861652image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
A simple visualization of nullity by column.
2024-04-01T12:59:43.518188image/svg+xmlMatplotlib v3.8.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellermmrsellingpricesaledate
02015KiaSorentoLXSUVautomatic5xyktca69fg566472ca5.016639.0whiteblackkia motors america inc20500.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
12015KiaSorentoLXSUVautomatic5xyktca69fg561319ca5.09393.0whitebeigekia motors america inc20800.021500.0Tue Dec 16 2014 12:30:00 GMT-0800 (PST)
22014BMW3 Series328i SULEVSedanautomaticwba3c1c51ek116351ca45.01331.0grayblackfinancial services remarketing (lease)31900.030000.0Thu Jan 15 2015 04:30:00 GMT-0800 (PST)
32015VolvoS60T5Sedanautomaticyv1612tb4f1310987ca41.014282.0whiteblackvolvo na rep/world omni27500.027750.0Thu Jan 29 2015 04:30:00 GMT-0800 (PST)
42014BMW6 Series Gran Coupe650iSedanautomaticwba6b2c57ed129731ca43.02641.0grayblackfinancial services remarketing (lease)66000.067000.0Thu Dec 18 2014 12:30:00 GMT-0800 (PST)
52015NissanAltima2.5 SSedanautomatic1n4al3ap1fn326013ca1.05554.0grayblackenterprise vehicle exchange / tra / rental / tulsa15350.010900.0Tue Dec 30 2014 12:00:00 GMT-0800 (PST)
62014BMWM5BaseSedanautomaticwbsfv9c51ed593089ca34.014943.0blackblackthe hertz corporation69000.065000.0Wed Dec 17 2014 12:30:00 GMT-0800 (PST)
72014ChevroletCruze1LTSedanautomatic1g1pc5sb2e7128460ca2.028617.0blackblackenterprise vehicle exchange / tra / rental / tulsa11900.09800.0Tue Dec 16 2014 13:00:00 GMT-0800 (PST)
82014AudiA42.0T Premium Plus quattroSedanautomaticwauffafl3en030343ca42.09557.0whiteblackaudi mission viejo32100.032250.0Thu Dec 18 2014 12:00:00 GMT-0800 (PST)
92014ChevroletCamaroLTConvertibleautomatic2g1fb3d37e9218789ca3.04809.0redblackd/m auto sales inc26300.017500.0Tue Jan 20 2015 04:00:00 GMT-0800 (PST)
yearmakemodeltrimbodytransmissionvinstateconditionodometercolorinteriorsellermmrsellingpricesaledate
5588272014JeepGrand CherokeeLaredoSUVautomatic1c4rjfag0ec466276pa42.025180.0grayblackhertz corporation/gdp26000.024500.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5588282012DodgeGrand CaravanAmerican Value PackageMinivanautomatic2c4rdgbg1cr349287ma37.097036.0silvergrayge fleet services for itself/servicer8300.07800.0Tue Jul 07 2015 06:30:00 GMT-0700 (PDT)
5588292012HyundaiElantraLimitedSedanNaN5npdh4ae7ch106397pa4.066720.0graygraychampion mazda10250.010400.0Wed Jul 08 2015 07:30:00 GMT-0700 (PDT)
5588302012NissanSentra2.0 SRSedanNaN3n1ab6ap3cl622485tn26.035858.0whitegraynissan-infiniti lt9950.010400.0Wed Jul 08 2015 17:15:00 GMT-0700 (PDT)
5588312011BMW5 Series528iSedanautomaticwbafr1c53bc744672fl39.066403.0whitebrownlauderdale imports ltd bmw pembrok pines20300.022800.0Tue Jul 07 2015 06:15:00 GMT-0700 (PDT)
5588322015KiaK900LuxurySedanNaNknalw4d4xf6019304in45.018255.0silverblackavis corporation35300.033000.0Thu Jul 09 2015 07:00:00 GMT-0700 (PDT)
5588332012Ram2500Power WagonCrew Cabautomatic3c6td5et6cg112407wa5.054393.0whiteblacki -5 uhlmann rv30200.030800.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5588342012BMWX5xDrive35dSUVautomatic5uxzw0c58cl668465ca48.050561.0blackblackfinancial services remarketing (lease)29800.034000.0Wed Jul 08 2015 09:30:00 GMT-0700 (PDT)
5588352015NissanAltima2.5 Ssedanautomatic1n4al3ap0fc216050ga38.016658.0whiteblackenterprise vehicle exchange / tra / rental / tulsa15100.011100.0Thu Jul 09 2015 06:45:00 GMT-0700 (PDT)
5588362014FordF-150XLTSuperCrewautomatic1ftfw1et2eke87277ca34.015008.0graygrayford motor credit company llc pd29600.026700.0Thu May 28 2015 05:30:00 GMT-0700 (PDT)